Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5354~5365
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5354-5365  5354
Journal homepage: http://ijece.iaescore.com
Techniques for predicting dark web events focused on the
delivery of illicit products and ordered crime
Romil Rawat1
, Olukayode Ayodele Oki2
, Sakthidasan Sankaran3
, Hector Florez4
,
Sunday Adeola Ajagbe5
1
Department of Computer and Information Technology, University of Extremadura, Badajoz, Spain
2
Department of Information Technology, Walter Sisulu University, East London, South Africa
3
Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, India
4
ITI Research Group, Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia
5
Department of Computer and Industrial Production Engineering, First Technical University Ibadan, Ibadan, Nigeria
Article Info ABSTRACT
Article history:
Received Aug 27, 2022
Revised Mar 10, 2023
Accepted Mar 12, 2023
Malicious actors, specially trained professionals operating anonymously on
the dark web (DW) platform to conduct cyber fraud, illegal drug supply,
online kidnapping orders, CryptoLocker induction, contract hacking, terrorist
recruitment portals on the online social network (OSN) platform, and
financing are always a possibility in the hyperspace. The amount and variety
of unlawful actions are increasing, which has prompted law enforcement (LE)
agencies to develop efficient prevention tactics. In the current atmosphere of
rapidly expanding cybercrime, conventional crime-solving methods are
unable to produce results due to their slowness and inefficiency. The methods
for accurately predicting crime before it happens "automated machine" to help
police officers ease the burden on personnel while also assisting in preventing
offense. To achieve and explain the results of a few cases in which such
approaches were applied, we advise combining machine learning (ML) with
computer vision (CV) strategies. This study's objective is to present dark web
crime statistics and a forecasting model for generating alerts of illegal
operations like drug supply, people smuggling, terrorist staffing and
radicalization, and deceitful activities that are connected to gangs or
organizations showing online presence using ML and CV to help law
enforcement organizations identify, and accumulate proactive tactics for
solving crimes.
Keywords:
Computer vision
Crime prediction
Cyber terrorism
Darkweb
Information security
Machine learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sunday Adeola Ajagbe
Department of Computer and Industrial Production Engineering, First Technical University Ibadan
Km 15 Lagos–Ibadan Expy, 200255, Ibadan, Oyo, Nigeria
Email: sunday.ajagbe@tech-u.edu.ng
1. INTRODUCTION
In the field of artificial intelligence (AI), computer vision (CV) and image processing frameworks are
used to identify and interpret the visual world, giving the machine a sense of awareness of its virtual cognitive
surroundings [1]–[3]. The modeling of actual criminal patterns and signature loops is made easier by CV [4],
[5]. By obtaining three-dimensional (3D) visuals in object detection, face and gesture recognition, image
computation, criminal image identification, terrorist location and weapons recognition, illicit activity
monitoring and alarming, geolocation tagging, and suspicious word scripts, mathematical approaches have
been developed to retrieve and make it possible for automated processes (AS) to interpret data [6], [7]. VLFeat
is a tool that can produce results much more quickly than anticipated [8], [9]. VLFeat was defined as a library
of as a library of CV algorithms in an artificial intelligence-machine learning (AI-ML) study that was utilized

Int J Elec & Comp Eng ISSN: 2088-8708 
Techniques for predicting dark web events focused on the delivery of illicit products and … (Romil Rawat)
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to carry out fast prototyping and identify the human posture using face detection and human identification [10].
A computer system may learn from past events despite needing to be expressly programmed using the machine
learning (ML) approach [11] and ML understands the precise architecture and frameworks [12], [13]. Although
the nature of various offenses and their motives often appear to be random, ML may aid with pattern
identification [14], [15] and content modelling utilizing natural language processing (NLP) techniques based
on CV.
Mahanolob is a cybercrime analysis and prediction tool with a dynamic time-wrapping technique that
enables both the forecast of crime and the eventual perpetrator's apprehension, according to related research
[1]. Furthermore, the law enforcement (LE) in the United States, United Kingdom, and other European nations
use crime-predicting apps to monitor criminal activity on social media and in specific geographic areas [16].
National authorities and the government now encourage the merging of ML techniques with technological
automated systems and criminal intelligence [17]. It provides the means of a brand-new, strong machine
(a group of programs) to aid in the pursuit of criminal investigations. The main objective of crime prediction
is to foresee incidents before they take place so that a prior plan may be developed in recognized terrorist and
criminal hotspots, which helps to comprehend terrorist action plans. Forecasting, policing with a high degree
of precision, government critical resources such as police manpower educated with cyber tools based on ML,
detectives, and financial specialists at cyber network usage, to battle crime.
Figure 1 outlines the background behaviors of illicit activities containing terrorist cyber events,
triggering modes, propagation modes, damaging factors, and structure of losses. Cyber vulnerabilities are
planned and created by terrorists in a sequential manner, identifying the effects on online platforms. The cyber
threat always triggers an evaluation of distributed factors. The purposes of triggering are to make the post-
global and attract supporters to join terrorist camps using the online social networks (OSN) platform.
The remainder of the section is laid out as follows: section 2 discusses terrorism diagnosis using social
media. Section 3 discusses crime anticipation using ML techniques. Section 4 discusses crime prediction
approaches CV, ML, deep learning (DL). Section 5 discusses proposed concept and design for cybercrime
prediction with crime statistics. Section 6 provides the results and discussion of this research while. And
section 7 concludes the paper with future work. Contribution: i) to show crime prediction using ML, CV, and
DL with crime statistics for tracing illicit events channels and criminals’ associations; ii) to show the hidden
criminal market business tracing; and iii) to help the law enforcement officials to trace criminal events on
digital platforms, so that action can be taken.
Figure 1. Terrorist cyber events triggering
2. TERRORISM DIAGNOSIS FROM SOCIAL MEDIA
Various techniques and automated engineers are being developed to detect terrorist content on social
media [18], [19]. Malicious data in the form of text, pictures, videos, audio, likes, and re-sharing of posts
spreads terrorist sentiments or infringements or messages for terror clusters, causing massive unrest and
disruptions in the state or country, particularly in certain regions used for spreading propaganda and recruiting
a terrorist army. Figure 2 shows the AI-based terrorist image behavior data prediction. Unethical posts related

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to terrorism and data are collected from online platforms for creating data stores so that features can be
extricated for further intelligent evaluation. The experimental data is collected by scarping the dark web
platform to generate defined fingerprints and criminal activities associated with them. Based on the generated
dataset, the model is trained for the prediction of all events relating to criminal activities, focusing on terrorist-
related actions.
Figure 3 shows the labelling of terrorism-related post and contents. The online platform is surrounded
by illicit activities, but it becomes difficult for normal users to identify and block them. So, terror-related
content is selected for labelling and the results are modelled using intelligent algorithms convolutional neural
network (CNN) and artificial neural network (ANN) [20], [21]. This helps the engines to automatically filter
the malicious posts resembling terror activities and makes the modelled (group) vulnerable [22] as it helps the
person sharing and resharing the post along with comments to highlight the information to the maximum
audience.
Figure 2. AI-based terrorist image behavior data prediction
Figure 3. Labeling of terrorism-related posts and contents

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3. CRIME ANTICIPATION USING ML TECHNIQUES
The comparative study was conducted using Weka, which is open an opensource tool for data mining.
Violent crime trends from the dataset of communities and crime unnormalized and real-time crime statistical
data based on three methods, namely linear regression (LR), additive regression (AR), and decision stump
(DS), were constructed utilizing similar limited sets of characteristics for demonstrating the efficacy of ML
approaches in predicting violent crime patterns of criminal hotspots, the test samples were chosen at random.
LR algorithm shows appreciable results among the listed algorithms and tolerates unpredictability in the test
data to some extent [23]. The crimes of house burglary, street robbery, and battery were examined
retrospectively using an ensemble model to synthesize the findings of logistic regression and neural network
(NN) frameworks using the predictive analytic approach to produce fortnightly and monthly forecasts (based
on previous three years of cybercrime datasets) for the year [1]. ML was used to examine crime predictions.
For the purpose of prediction, crime statistics from the previous 15 years in Vancouver (Canada) were studied.
The accumulation of data, data categorization, pattern recognition, prediction, and visualization are all part of
ML-based criminal investigations. The crime dataset was further analyzed using boosted decision tree (BDT)
and k-nearest neighbor (KNN) methods. In a separate but similar research, [24], [25] looked at 560,000 crime
statistics from 2003 to 2018 and found that using ML algorithms for crime prediction, the studies predicted
crime with an accuracy of 44 per cent to 39 per cent respectively.
The crime dataset from Chicago, the United States. ML and data science (DS) approaches were
applied to predict crime details consisting of parameters (scene positioning, type, date, time, and coordinates).
decision trees (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and Bayesian
techniques (BT) are used, with the most accurate model training. With an accuracy of about 0.787, the KNN
classification proved to be the most accurate. The authors also utilized several graphics to assist in
comprehending the various features of the Chicago crime dataset to better anticipate, identify, and solve crimes,
resulting in a reduction in the crime rate. Data (taken from Chicago crime statistics, demographic and climatic
data) accumulation, data preprocessing, predictive model development, dataset training, and testing are
included in the proposed system to demonstrate the efficacy of the ML system to forecast violent behaviors,
and crime incidences, and precise attributes of criminals. A deep neural network (DNN) forecasts crime
attributes and occurrences by combining feature-based multi-model data from the environmental context. ML
approaches like regression analysis (RA), kernel density estimation (KDE), and SVM is used in crime
prediction systems [26], [27]. Figure 4 presents the dataflow diagram.
Figure 4. Dataflow diagram
The suggested DNN has an accuracy of 84.25%, whereas the SVM and KDE have an accuracy of
67.01% and 66.33%, indicating that the suggested DNN was much more accurate than the other prediction
models in predicting crime occurrences [5]. The data were analyzed and interpreted using approaches such as
Bayesian neural networks (BNN), and the Levenberg Marquardt algorithm (LMA) [12], and a scaled algorithm,
with the scaled algorithm outperforming the other approaches. Statistical analysis revealed that using the scaled
method, the crime rate could be reduced by 78%, implying an accuracy of 0.78. RapidMiner was used in a
prediction study utilizing ML and historical crime trends in data collection, preparation, analysis, and
visualization in the four primary visualization studies [9]. Big data (BD) offers a high throughput and fault
tolerance, analyzing huge datasets and providing accurate findings, whilst the ML-based naive Bayes (NB)

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method can make superior predictions with the existing datasets. Various data mining (DM) and ML methods
utilizable singminal investigations are presented [6]. This study contributes by emphasizing the techniques
utilized in crime data analytics. The grid-based crime forecasting framework created a series of spatial-
temporal characteristics for a city in Taiwan based on 84 identified geographic locations for anticipating crime
in the next slot (month) for every grid. DNN was determined to be the best model among the numerous ML
techniques, particularly for a feature and attribute learning [28]. Furthermore, the suggested model architecture
exceeded the baseline in terms of crime displacement testing. Figure 5 presents the functionality of the
proposed approach.
Figure 5. Functionality of the proposed approach
4. CRIME PREDICTION APPROACHES (CV, ML, DL)
Alves et al. [29] demonstrated that integrating grey correlation analysis based on a new weighted
k-nearest neighbor (GBWKNN) filling technique with KNN classification improves crime prediction accuracy.
Using the suggested method, the study achieved a 67% accuracy rate. Obuandike et al. [30] classified crime
data into two categories based on complexity, with the KNN method achieving an accuracy of approximately
87%.
Rajesh et al. [18] presented an insight into data mining and ML algorithms using an international
database. With the help of Python and Jupyter Notebook, patterns and predictions were displayed as
visualizations. This analysis aided in the development of suitable counter-terrorism measures, as well as
increased investments, economic growth, and tourism. random forest regressor (RFR) outperformed all other
ML algorithms considered in the study. Using the DT method, [31] obtained an accuracy of 84%. However, in
both situations, a minor change in the data might result in a significant change in the structure. A novel crime
detection approach known as naive Bayes (NB) is used for crime prediction and analysis [32]–[34]. Comes
[11] only had an accuracy rate of 66% in predicting crimes and did not take into account computing speed,
resilience, or scalability which are also important.
The multi-camera model of video surveillance was so well-designed that it can handle all three key
tasks for normal police "stake-out", namely detection, representation, and recognition [35]–[37]. The detecting
section combines video feed from numerous cameras to extract motion trajectories from videos quickly and
accurately. The representation aids in the completion of raw trajectory data in order to create hierarchical,
invariant, and content-rich motion event descriptions. Finally, the recognition section deals with event
classification (such as robbery, as well as possible murder and molestation) and data descriptor identification.
They created a sequence-alignment kernel function to perform sequence data learning to detect suspicious or
possible criminal occurrences for effective recognition. A technique was proposed for distinguishing
individuals for espionage using a novel feature called soft biometry, which incorporates a person's height,
build, facial features, shirt and trousers color, motion behavior, and trajectory record to recognize and monitor
passengers, as well as forecast crime pursuits and deal with some strange human error scenarios where the
perpetrators get away with it [38]. They also conducted examinations with the findings being publicized.
People's behaviors are captured, offering piggyback rides in increasingly remote locations with a given
sequence from event footage. Table 1 summarizes the comparative study of crime prediction techniques with
their accuracy and related findings. In Table 1, we summarized the evaluation models, further demonstrating
qualitative analysis and accuracy.
Crime hotspots, known as severe-crime zones, have a high probability of crime occurrence and present
abnormal events with a high likelihood of detecting criminals. They performed research on predicting crime

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hotspots and implemented their model with google tensor flow. The emphasis is to produce higher value to
demonstrate that the technique is more effective. with similar evaluation parameters, the gated recurrent unit
(GRU) and long short-term memory (LSTM), achieved accuracy (81.5%), precision (86.5%), recall (75%), and
an F1-score (0.8). Both outperform the standard recurrent neural network (RNN) version by a wide margin.
The GRU version showed 2% better performance compared to RNN at receiver operating characteristic (ROC)
area under the curve (AUC) findings. LSTM received the highest AUC score, which was 3% higher than the
GRU version. A spatiotemporal crime network (STCN) is presented [36] which uses a CNN to predict crime
before it happens. From 2010 to 2015, the authors used New York felony datasets (number-311) to test the
STCN. The STCN outperformed the four baselines with an F1-score (88%) and an AUC (92%). Their
suggested model outperformed the other baselines by F1-score and AUC values, and even when the time
window approached 100, it was still better than the others in terms of the effectiveness of working in a densely
populated region.
Table 1. Crime prediction techniques
No Crime prediction
techniques with references
Accuracy Findings
1 RFR [18] 97% High accuracy in previously recorded crimes.
2 DT [15] 83.95% The DT shows good efficiency than NB, along with the same crime
dataset implemented on Weka.
3 KNN (K=10) [39] 87.03% Data has compared to five classification algorithms, finding that the
NB, NN, and KNN algorithms have a better prediction rate than SVM
and DT algorithms.
4 Decision tree (J48) [40] 59.15% Experiments were done on J48 naïve Bayesian and ZeroR by
comparing them.
5 NB [16] 65.59% The comparative study is done based on the accuracy of k-NN, NB,
and DT for the prediction of crimes and criminal behaviors.
6 Naïve Bayes classifier [28] 87.00% NB is used for crime analysis and prediction.
7 SVM [29] 84.37%. Several models have been compared for analyzing the best chance of
predicting hotspots.
8 KNN (K=5) [32] 66.69% By combining GBWKNN and KNN classification approaches better
accuracy is achieved.
9 Proposed word 89.50% Focused on predicting the crime using ML, CV, and DL using crime
statistics for tracing Illicit events channels and criminals’ associations.
5. PROPOSED CONCEPT AND DESIGN FOR CYBERCRIME PREDICTION WITH CRIME
STATISTICS
We assessed the relevance of each approach after discovering and comprehending numerous diverse
ways utilized by security agencies for surveillance reasons. Every surveillance method generates appreciable
results when found actively engaged in communication, like the sting ray used for detecting the geolocation of
a user. So, to track the location based on replicating human approaches continually by self-updating modeling
approach, even though communication is not made, a modern intelligent framework modeling DL, ML, and
CV algorithms for conducting surveillance [41]–[45]. Table 2 contains the key components and processes of
the proposed system. Table 2 contains the key components and processes of the proposed system.
By combining all these capabilities during a preliminary round, we would like to employ closed circuit
television (CCTVs) connected to intelligent automated systems in real-world settings to comprehend the
previously recorded crimes (collected Instances is 8,000), using ML and DL approaches for greater knowledge
of criminality (explaining how, why, and where). We do not just propose building a world-class model to
anticipate crimes; we propose teaching it to comprehend prior crimes in order to better assess and forecast them
based on the utilization of scenario simulations. Following an analysis of the scene and the use of the key
features listed above, the program should conduct at least 90 simulations of the current scenario in front of it,
with the help of previously learned criminal records, to determine and recommend a plan of strategy for alerting
LE personnel. In Figure 6, we provide the terrorist and criminals presence detection models.
- Input tracking: Data is collected from drones, static cameras, voice, and recording devices focused at
suspicious places.
- Mapping with database: Containing profile and features of crime in security agency's databases relating to
dark web (unusual weapon image, suspected criminal image, drug dealers, gangs’ tattoos or marks, financial
fraudulent agent).
- Automated engines: It will search the online presence of these criminals, for mapping with the site, so that
the website and owner activity can be tracked.
- Alert of association: It is generated towards cyber cells or related authorities for collecting evidence.

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- Dynamic database of security agencies connected with OSN: Containing CNN for crawling vulnerable
posts, text, images, and video at OSN to map with Input tracking data [46], [47].
Table 2. Key components and processes of the systems
Components Processes
Root analytics  Knowing the number of statistical methodologies able to anticipate future
events.
 The instance may range from behavioral intuition to robbing an organization
in future timeframes.
Neural networks  Consisting of a huge series of algorithms that assist in the discovery of data
relationships by behaving and associating human cognition.
 Replicating biological nerve cells, attempting to think for it.
 Anticipating a crime scene.
Automated intelligent engines  Engines that must fingerprint antivirus and viruses.
 Improving the security of the system by identifying the type of threat and
eliminating it using recognized antivirus.
 Continuity of machine’s surveillance in case of broken down.
 Prediction of anomaly time series prediction, and decisive approach with
uncertainty.
 Data mining in the detection of patterns in criminal’s activity.
Cryptographic algorithms  Encrypting the known confidential criminal data in a secure manner.
 Utilized to encrypt newly found possible criminal data.
Cyber threat detection and
classification
 Classification of threats and criminal conduct like probable terrorist attacks
can be anticipated based on the timeline.
Forensic evidence  Organize, analyze, and learn from the data once it has been collected.
NLP  Suspicious Speech print identification.
 Identification of cyber criminal’s language and comprehension based on
specific features represented using a mathematical formula.
Data collection and analysis  Knowing previous crime attributes for casting future crime prediction rates.
Gait analysis  To understand posture when walking and research human motion.
 To gain a better understanding of a person's usual pace and body mark.
Features  To determine an unusual visit to the criminal zone at a specific period,
allowing the system to notify authorities.
The scale of the dark web marketplaces (Silkroad, Alpha Bay, and Pandora) economy was difficult to
determine and was growing all the time. Researchers estimated the Silkroad's sales volume at $360,000 each
day based on scrapes and comments, equating to more than $120 million in a year [48]. The requirements for
meeting the supply of illicit orders generated through dark web platforms are detailed in the Table 3. Our
proposed model helps to track the activities of these associated criminals and agents contacting customers for
delivery, thereby reaching out to the chain of order and criminal events. Table 3 presents the classification,
dealers, agents and percentages of our system, the confusion matrix, and the outlines of graphical statistics of
crime associated with the dark web environment are presented in Figures 7 and 8 respectively. The Table 4 is
performance metrics and outcomes.
Figure 6. Terrorist and criminals’ presence detection model

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Table 3. Dealers and agents meeting chart for illicit business trade and supply
Classification Point of meeting - contact required (dealers and agents) Percentage
Online gambling No 1.7
Weapons trade Yes 2.3
Criminal chat forums May be 2.2
Pornography Yes 3.5
Financial fraud May be 4.9
Anonymity May be 4.7
Ransomware No 3.5
Prostitution Yes 5.3
Human trafficking Yes 5.8
Organ trafficking Yes 5.1
Whistleblower No 4.5
Drug trade Yes 5.2
Financial fraud May be 7.3
Contract killing Yes 1.3
Gangs of Influence Yes 2.3
Live streaming of criminals’ events Yes 3.8
Terrorism propaganda sharing No 5.6
Terrorist recruitment and radicalization Yes 3.4
Sale of antiques Yes 2.8
cyber extortion Yes 3.5
Hacking No 5.2
Cyber-attack activation No 5.3
Industrial applications controlling May be 5.2
others May be 5.6
Figure 7. crime statistics confusion matrix
Figure 8. Crime statistics on dark web platform

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Table 4. Performance metrics and outcomes
S/N Measure Descriptions Outcomes
1 Sensitivity 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝑇𝑃𝑅) = 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝑇𝑃)/(𝑇𝑃 + 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 (𝐹𝑁)) 0.7383
2 Specificity 𝑆𝑃𝐶 = 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒(𝑇𝑁)/(𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝐹𝑃) + 𝑇𝑁) 0.9384
3 Precision 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 (𝑃𝑃𝑉) = 𝑇𝑃/(𝑇𝑃 + 𝐹𝑃) 0.8650
4 Negative predictive value 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 (𝑁𝑃𝑉) = 𝑇𝑁/(𝑇𝑁 + 𝐹𝑁) 0.9027
5 False positive rate 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝐹𝑃𝑅) = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑁) 0.7116
6 False discovery rate 𝐹𝑎𝑙𝑠𝑒 𝑑𝑖𝑠𝑐𝑜𝑣𝑒𝑟𝑦 𝑟𝑎𝑡𝑒 (𝐹𝐷𝑅) = 𝐹𝑃/(𝐹𝑃 + 𝑇𝑃) 0.7959
7 False negative rate 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 (𝐹𝑁𝑅) = 𝐹𝑁/(𝐹𝑁 + 𝑇𝑃) 0.6817
8 Accuracy 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (𝐴𝐶𝐶) = (𝑇𝑃 + 𝑇𝑁)/(𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 (𝑃)
+ 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 (𝑁))
0.8950
9 F1-score 𝐹1 = 2𝑇𝑃/(2𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁) 0.8001
6. RESULT AND DISCUSSION
The comparison of fortnightly projections of monthly analytical predictions with divides into
day-night datasets, the researchers found, greatly improved the results. Due to its secrecy, the dark web has
long been a target for criminals looking to make money illegally abroad. The current work uses ML, CV, and
DL to forecast crime, and crime stats are offered to track criminal networks and compare the comparative
research with the aspects of the suggested strategy that have been put into practice. The research is based on a
fictitious model for locating terrorists and lawbreakers operating on the dark web who are engaged in drug
dealing, human trafficking, staffing of terrorists, distribution of weapons, execution orders delivered online,
and other illegal activities linked to gangs or organizations with active websites. Utilizing automated machine
characteristics, modeling, and recognition. This experiment is about scraping the dark web site generates
specific signatures and the illicit behaviors connected to them, which is how the exploratory data is gathered.
The system is trained to forecast all criminal activity-related occurrences, with an emphasis on terrorist-related
behaviors, using the provided dataset [49]. No such dataset exits contain records of criminals’ events and
channels like (drug supply, human trafficking, terrorist radicalization and recruitment, weapon delivery, online
killing orders, and fraudulent activities associated with gangs or organizations showing online presence). The
proposed focused on the work of hypothetical model and covered multidimensional illicit events channels with
machine learning and computer vison technique [50].
Image processing technique and feature extraction utilizes ImageNet, one of the largest datasets of
annotated pictures, CNN, a deep learning model that has been essential in enhancing computer vision, learns
patterns that typically appear in images and is then equipped to adjust as new data is analyzed. Both a feature
detector and a feature descriptor, spectrum feature transform (SIFT). SIFT splits an image into a vast number
of localized characteristic vectors, all of which is somewhat robust to changes in light and affine or 3D
projection as well as invariant to image translation, scaling, and rotation. Computer vision linking with image
processing: AI and pattern identification methods for crime prediction are used in the domains of CV and image
processing to acquire Illicit event sequences for extracting useful knowledge from photos, videos, and other
visual inputs. One of the numerous methods used in CV is image synthesis, but other methods as well, including
ML, CNN, and so on, are also used. One of the subfields in the science of CV is image processing and belongs
to the subfield of image computing.
7. CONCLUSION AND FUTURE WORK
The authors concluded that comparing fortnightly forecasts of monthly analysis predictions with splits
into day-night datasets improved the results significantly. Due to its anonymity, the dark web has always
attracted the interest of criminals interested in generating illicit revenues across borders. The present work
predicts crime using ML, CV, and DL with crime statistics to track criminal chains and compare the
comparative study with the implemented features of the given approach. The work is based on a hypothetical
model for tracking dark web criminals and terrorists involved in drug supply, human trafficking, terrorist
radicalization and recruitment, weapon delivery, online killing orders, and fraudulent activities associated with
gangs or organizations showing an online presence. The mapping and identification using automated machine
features will help security agencies investigate the root suppliers of prohibited and illegal items. The
anonymous dark web platform changes with hosting, so it takes time to track it. But criminals also use
digital platforms for promotion or marketing tactics to supply or attract other criminals. Based on digital traces
and evidence, security agencies can track the network. Our future research will begin with the creation of a
machine that can predict and recognize patterns based on geo-location coordinates and the dates of similar
crimes. We also hope to create software that can act as a universal security official, with eyes and ears
everywhere.

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BIOGRAPHIES OF AUTHORS
Romil Rawat is a research scholar and attended several research programs and
received research grants from USA, Germany, Italy, and UK. The author has research alignment
towards cyber security, internet of things, dark web crime analysis and investigation techniques,
and working towards tracing of illicit anonymous contents of cyber terrorism and criminal
activities. He also chaired international conferences and hosted several research events
including national and International Research Schools, PhD colloquium, workshops, training
programs. He also published several research patents. He can be contacted at
rawat.romil@gmail.com and rrawatna@alumnos.unex.es.
Olukayode Ayodele Oki received his PhD from the University of Zululand, South
Africa in 2019. He is a lecturer in the Department of Information Technology at Walter Sisulu
University, South Africa. He has authored more than 30 articles. His research interests include
biologically inspired computation, ICT4D, communication networks, internet of things,
machine learning, data analytics and climate-smart agriculture. He has received several grants
both for research and amp; development and to attend conferences. He is a recipient of the South
Africa National Research Foundation (NRF) rated researcher award, an honorary rosalind
member of the London journal press and a member of the IEEE South Africa subsection. He
can be contacted at ooki@wsu.ac.za.

5365
Sakthidasan Sankaran is a Professor in the Department of Electronics and
Communication Engineering at Hindustan Institute of Technology and Science, India. He
received his B.E. degree from Anna University in 2005, M.Tech. Degree from SRM University
in 2007 and Ph.D. Degree from Anna University in 2016. He is a senior member of IEEE for
the past 10 years and a member of various professional bodies. He is an active reviewer in
Elsevier journals and an editorial board member in various international journals. His research
interests include image processing, wireless networks, cloud computing and antenna design. He
has published more than 70 papers in Referred journals and International Conferences. He has
also published three books to his credit. He can be contacted at sakthidasan.apec@gmail.com.
Hector Florez obtained Ph.D. in Engineering, M.Sc. in Information and
Communication Sciences, M.Sc. in Management, B.Sc. in Electronic Engineering, B.Sc.
in Computing Engineering, and B.Sc. in Mathematics. He is a full professor at the Francisco
Jose de Caldas District University, Bogota Colombia. He can be contacted at email:
haflorezf@udistrital.edu.co.
Sunday Adeola Ajagbe is a Ph.D candidate at the Department of Computer
Engineering, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria and
a Lecturer, a First Technical University, Ibadan, Nigeria. He obtained MSc and BSc in
Information Technology and Communication Technology respectively at the National Open
University of Nigeria (NOUN), and his Postgraduate Diploma in Electronics and Electrical
Engineering at LAUTECH. His specialization includes Artificial Intelligence (AI), Natural
language processing (NLP), Information Security, Data Science, and the Internet of Things
(IoT). He is also licensed by The Council Regulating Engineering in Nigeria (COREN) as a
professional Electrical Engineer, a student member of the Institute of Electrical and Electronics
Engineers (IEEE), and International Association of Engineers (IAENG). He has many
publications to his credit in reputable academic databases. He can be contacted at email:
sunday.ajagbe@tech-u.edu.ng.

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